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» Learning with the Set Covering Machine
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120
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COLT
2005
Springer
15 years 9 months ago
Separating Models of Learning from Correlated and Uncorrelated Data
We consider a natural framework of learning from correlated data, in which successive examples used for learning are generated according to a random walk over the space of possibl...
Ariel Elbaz, Homin K. Lee, Rocco A. Servedio, Andr...
ICML
2000
IEEE
16 years 4 months ago
Discovering Homogeneous Regions in Spatial Data through Competition
If all features causing heterogeneity were observed, a mixture of experts approach (Jacobs et al., 1991) is likely to be superior to using a single model. When unobserved or very n...
Slobodan Vucetic, Zoran Obradovic
131
Voted
ICML
1998
IEEE
16 years 4 months ago
An Efficient Boosting Algorithm for Combining Preferences
We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences aris...
Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yora...
106
Voted
ICML
2010
IEEE
15 years 4 months ago
Deep Supervised t-Distributed Embedding
Deep learning has been successfully applied to perform non-linear embedding. In this paper, we present supervised embedding techniques that use a deep network to collapse classes....
Martin Renqiang Min, Laurens van der Maaten, Zinen...
ICMLA
2009
15 years 1 months ago
Structured Prediction Models for Chord Transcription of Music Audio
Chord sequences are a compact and useful description of music, representing each beat or measure in terms of a likely distribution over individual notes without specifying the not...
Adrian Weller, Daniel P. W. Ellis, Tony Jebara